import torch
import torch.nn as nn
import numpy as np
from codes.misc_fucntion import convert_to_grayscale, save_gradient_images, get_positive_negative_saliency

"""
    Usage:
    target_example=0
    original_image, prep_img, target_class, file_name_to_export, pretrained_model
    GBP = GuidedBackprop(pretrained_model)
    guided_grads = GBP.generate_gradients(prep_img, target_class)
    save_gradient_images(guided_grads, file_name_to_export + '_Guided_BP_color')
    
    grayscale_guided_grads = convert_to_grayscale(guided_grads)
    save_gradient_images(grayscale_guided_grads, file_name_to_export + '_Guided_BP_gray')
    
    # Positive and negative saliency maps
    pos_sal, neg_sal = get_positive_negative_saliency(guided_grads)
    save_gradient_images(pos_sal, file_name_to_export + '_pos_sal')
    save_gradient_images(neg_sal, file_name_to_export + '_neg_sal')
"""


class GuidedBackprop():
    """
       Produces gradients generated with guided back propagation from the given image
    """

    def __init__(self, model):
        self.model = model
        self.gradients = None
        self.forward_relu_outputs = []
        # Put model in evaluation mode
        self.model.eval()
        self.update_relus()
        self.hook_layers()

    def hook_layers(self):
        def hook_function(module, grad_in, grad_out):
            self.gradients = grad_in[0]

        # Register hook to the first layer
        first_layer = list(self.model._modules.items())[0][1]
        first_layer.register_backward_hook(hook_function)

    def update_relus(self):
        """
            Updates relu activation functions so that
                1- stores output in forward pass
                2- imputes zero for gradient values that are less than zero
        """

        def relu_backward_hook_function(module, grad_in, grad_out):
            """
            If there is a negative gradient, change it to zero
            """
            # Get last forward output
            corresponding_forward_output = self.forward_relu_outputs[-1]
            corresponding_forward_output[corresponding_forward_output > 0] = 1
            modified_grad_out = corresponding_forward_output * torch.clamp(grad_in[0], min=0.0)
            del self.forward_relu_outputs[-1]  # Remove last forward output
            return (modified_grad_out,)

        def relu_forward_hook_function(module, ten_in, ten_out):
            """
            Store results of forward pass
            """
            self.forward_relu_outputs.append(ten_out)

        # Loop through layers, hook up ReLUs

        # for my own ResNet34 model
        for module in self.model.named_modules():
            if isinstance(module[1], nn.ReLU):
                module[1].register_backward_hook(relu_backward_hook_function)
                module[1].register_forward_hook(relu_forward_hook_function)
            elif isinstance(module[1], nn.Sequential):
                for sub_module in module[1].named_modules():
                    if isinstance(sub_module[1], nn.ReLU):
                        sub_module[1].register_backward_hook(relu_backward_hook_function)
                        sub_module[1].register_forward_hook(relu_forward_hook_function)
                    elif isinstance(sub_module[1], nn.Sequential):
                        for subsub_module in sub_module[1].named_modules():
                            if isinstance(subsub_module[1], nn.ReLU):
                                subsub_module[1].regi ster_backward_hook(relu_backward_hook_function)
                                subsub_module[1].register_forward_hook(relu_forward_hook_function)

        # for pos, module in self.model._modules.items():
        #     if isinstance(module, nn.ReLU):
        #         module.register_backward_hook(relu_backward_hook_function)
        #         module.register_forward_hook(relu_forward_hook_function)

    def generate_gradients(self, input_image, target_class):
        # Forward pass
        model_output = self.model(input_image)
        if target_class is None:
            target_class = np.argmax(model_output.data.cpu().numpy())
        # Zero gradients
        self.model.zero_grad()
        # Target for backprop
        one_hot_output = torch.FloatTensor(1, model_output.size()[-1]).zero_().cuda()
        one_hot_output[0][target_class] = 1
        # Backward pass
        model_output.backward(gradient=one_hot_output)
        # Convert Pytorch variable to numpy array
        # [0] to get rid of the first channel (1,3,224,224)
        gradients_as_arr = self.gradients.data.cpu().numpy()[0]
        return gradients_as_arr
